Flow‐substrate interactions create and mediate leaf litter resource patches in streams
Bibliographic record
Abstract
Summary 1. The roles that streambed geometry, channel morphology, and water velocity play in the retention and subsequent breakdown of leaf litter in small streams were examined by conducting a series of field and laboratory experiments. 2. In the first experiment, conditioned red alder (Alnus rubra Bongard) leaves were released individually in three riffles and three pools in a second‐order stream. The transport distance of each leaf was measured. Several channel and streambed variables were measured at each leaf settlement location and compared with a similar number of measurements taken at regular intervals along streambed transects (‘reference locations’). Channel features (such as water depth) and substrate variables (including stone height, stone height‐to‐width ratio, and relative protrusion) were the most important factors in leaf retention. 3. In the second experiment, the role of settlement location and reach type in determining the rate of leaf litter breakdown was examined by placing individual conditioned red alder leaves in exposed and sheltered locations (on the upper and lower edges of the upstream face of streambed stones, respectively) in riffle and pool habitats. After 10 days, percent mass remaining of each leaf was measured. Generally, leaves broke down faster in pools than in riffles. However, the role of exposure in breakdown rate differed between reach types (exposed pool > sheltered pool > sheltered riffle > exposed riffle). 4. In the third experiment, the importance of substrate geometry on leaf litter retention was examined by individually releasing artificial leaves upstream of a series of substrate models of varying shape. Substrates with high‐angle upstream faces (were vertical or close to vertical), and that had high aspect ratios (were tall relative to their width), retained leaves more effectively. 5. These results show that streambed morphology is an important factor in leaf litter retention and breakdown. Interactions between substrate and flow characteristics lead to the creation of detrital resource patchiness, and may partition leaf litter inputs between riffles and pools in streams at baseflow conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".